PICS: A Sequential Approach to Obtain Optimal Designs for Nonlinear Models Leveraging Closed-Form Solutions for Faster Convergence

D-Optimal designs for estimating parameters of response models are derived by maximizing the determinant of the Fisher information matrix. For a nonlinear model, the Fisher information matrix depends on the unknown parameter vector of interest, leading to a weird situation that in order to obtain the D-optimal design, one needs to have knowledge of the parameter to be estimated. One solution to this problem is to choose the design points sequentially, optimizing the D-optimality criterion using parameter estimates based on available data, followed by updating the parameter estimates using maximum likelihood estimation. On the other hand, there are many nonlinear models for which closed-form results for D-optimal designs are available, but because such solutions involve the parameters to be estimated, they can only be used by substituting “guesstimates” of parameters. In this article, a hybrid sequential strategy called PICS (Plug into closed-form solution) is proposed that replaces the optimization of the objective function at every single step by a draw from the probability distribution induced by the known optimal design by plugging in the current estimates. Under regularity conditions, asymptotic normality of the sequence of estimators generated by this approach are established. Usefulness of this approach in terms of achieving greater efficiency of estimation and resource saving compared to the standard sequential approaches are demonstrated with simulations conducted from two different sets of models.